利用当地公交网络实现最佳连续时空覆盖的驾车环境传感策略

Mayar Ariss, An Wang, Sadegh Sabouri, Fabio Duarte, Carlo Ratti
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引用次数: 0

摘要

对空气污染、二氧化碳排放、噪音和热量等环境特征的监测,为城市提供了关键的数据驱动见解,为可持续政策和城市设计提供建议。然而,鉴于环境数据的高度可变性,实现良好的时空分辨率和覆盖范围仍然是一项重大挑战。即使在阿姆斯特丹等监测良好的城市,环境传感器通常也只放置在极少数固定地点,这意味着空间覆盖范围有限,无法适应城市环境的变化。随着城市的发展,污染源也会发生变化,如果不进行昂贵而耗时的重新配置,固定传感器可能无法充分捕捉到这些变化。为了监测阿姆斯特丹道路的环境质量,我们提出了一种针对结构化车辆网络的 "驾驶式 "传感解决方案,这意味着传感器被设计部署在公共汽车和有轨电车上,而这些车辆的行驶轨迹和时间表都是已知的。我们提出了一种部署策略,结合现有的车队,针对不同的环境特征实现最佳时空覆盖。例如,通过优化公共交通车辆上传感器的部署,我们的建议大大加强了对阿姆斯特丹污染敏感区域的监测。根据所需的时空粒度,并注意到一辆车只能安装一个传感器,结构化网络上所需部署的传感器数量在 43 到 142 个之间,后者可实现最精细的分辨率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drive-by Environmental Sensing Strategy to Reach Optimal and Continuous Spatio-Temporal Coverage Using Local Transit Network
Monitoring environmental features, such as air pollution, carbon dioxide emissions, noise, and heat, gives cities key data-driven insights to advise sustainable policies and city design. However, given the high variability of the environmental data, achieving good spatio-temporal resolution and coverage remains a major challenge. Even in well-monitored cities, such as Amsterdam, environmental sensors are usually placed in very few fixed locations, implying limited spatial coverage and an inability to adapt to changes in the urban environment. As cities evolve, they experience shifts in pollution sources, and fixed sensors might not adequately capture these changes without a costly and time-consuming reconfiguration process. To monitor the environmental qualities of Amsterdam’s roads, we present a “drive-by” sensing solution for a structured network of vehicles, meaning that sensors are designed to be deployed on buses and tramways, the trajectories and schedules of which are known. We propose a deployment strategy that combines the available fleets to reach optimal spatio-temporal coverage for different environmental features. For example, by optimizing the deployment of sensors on public transit vehicles, our proposal significantly enhances the monitoring of pollution-sensitive areas in Amsterdam. Depending on the desired spatio-temporal granularity and noting that one vehicle only hosts one sensor, the required number of sensors to be deployed on the structured network varies between 43 and 142, with the latter achieving the finest possible resolution.
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